Kakoune is a modal editor. That means when you’re editing text, you’re constantly interacting with the editor using a powerful language of keystrokes — moving around, selecting text, transforming content, all through short, expressive commands typed directly in the editing interface. This is the core editing experience, and it’s what gives Kakoune its speed and fluidity.

But this isn’t the only way to interact with Kakoune.

There’s another side to Kakoune — a more structural, programmable one — which lets you interact with the editor through commands. These commands can be used to configure Kakoune, automate behavior, and define complex workflows. Commands can be written in configuration files, invoked via custom keybindings, or typed directly into the command prompt.

This document focuses on that second side: the command interface. We will explore how commands work, how they are built, and how Kakoune interprets them

Guía para ejecutar Claude Code usando modelos de Ollama (local o cloud) enfocado a codificación.

• Descarga de Ollama: https://ollama.com/download
• Documentación oficial: https://docs.ollama.com/integrations/claude-code

El comando ollama launch es nuevo. Si tienes una versión antigua (por ejemplo 0.11.x), verás:

Projects using Jev, TypeSafe AI's System One decision model (released 2026-09-15), in investment, trading, and financial-data contexts. Surveyed 2026-09-20 via GitHub API and community awesome-lists.

• jarrodwatts/jev-trader (★1.3k, 2026-09-16) — One AI trade decision every Monad block (~300 ms). Jev reads the Kuru MON-USDC order book and answers buy or sell; the bot posts a post-only limit order one tick inside the touch, earning the spread. Bun/TypeScript, dry-run mode, SSE dashboard. The template most projects below derive from.

This has been updated for 26.1 - 26.2, all of the optimization mods mentioned here are compatible with each other and don't affect vanilla behaviour by default.

Actúa como mi profesor particular de programación mientras estudio el ciclo de Desarrollo de Aplicaciones Web. Tu objetivo NO es resolver mis ejercicios: es que yo aprenda a resolverlos y que entienda cómo funcionas tú.

REGLAS SOBRE EL CÓDIGO

1. No me des nunca la solución completa a la primera, ni aunque te la pida en el primer mensaje.
2. Antes de responder, hazme una o dos preguntas para saber qué he intentado ya y dónde creo que está el problema.
3. Ayúdame por niveles y espera mi respuesta entre uno y otro:
   • Nivel 1: una pista conceptual, sin código.
   • Nivel 2: señálame la línea o la zona donde está el fallo y pregúntame qué crees que pasa ahí.

• Nivel 3: un ejemplo parecido pero con otro caso, nunca con mi ejercicio.

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.